LQG Control Design Research Articles

Robust LQG Controller Design by LMI Approach of a Doubly-Fed Induction Generator for Aero-Generator

Robust LQG Controller Design by LMI Approach of a Doubly-Fed Induction Generator for Aero-Generator

PI-shaped LQG control design for adaptive optics systems

Adaptive optics systems are extensively used in astronomy to obtain diffraction-limited images of celestial objects with ground-based telescopes. The crucial point is to control a deformable mirror to compensate for the distortions introduced by atmospheric turbulence in the incoming wavefront. In this paper, a modal PI-shaped LQG controller taking advantage of model knowledge is designed; it compares favorably to standard PI and Kalman-based controllers used in state-of-the-art control systems of telescopes. The proposed architecture is validated with simulations and experiments on a laboratory adaptive optics test-bench.

Backstepping LQG controller design for stabilizing and trajectory tracking of vehicle suspension system

The aim of this paper is to design a backstepping linear quadratic Gaussian controller (BLQGC) for a vehicle suspension (VS) system to improve the ride comfort by absorbing the shocks due to a rough and uneven road. For designing of the BLQGC, a forth order linearized model of the VS system is considered. In this control strategy, the conventional linear quadratic regulator is re-formulated with a state estimator based on the backstepping control approach to improve the control performance. The validation of improved control performance of BLQGC is established by comparative result investigation with other published control algorithms. The comparative results clearly reveal the better performance of the proposed approach to control the oscillation of the VS system within a stable range in terms of accuracy, robustness and handling uncertainties.

Design of backstepping LQG controller for blood glucose regulation in type I diabetes patient

Design of backstepping LQG controller for blood glucose regulation in type I diabetes patient

Design of backstepping LQG controller for blood glucose regulation in type I diabetes patient

The mechanisation of the insulin infusion regulation through the artificial pancreas (AP) is needed to design with an objective to control the blood glucose (BG) level in type 1 diabetes mellitus (T1DM) patients. However, to make it applicable in real time, major components needed are availability of proper sensor augmented pumps, glucose monitoring systems, and control techniques. Recent times many researchers suggest robust control techniques for designing a robust controller for computing the required insulin dose for a highly nonlinear human metabolism system. This paper proposes a simulation model of glucose metabolism process and design of a backstepping linear quadratic Gaussian controller (BLQGC) to control the BG level in TIDM patients. The simulations are carried out through MATLAB/SIMULINK environment and the results indicate comparatively better control ability of the proposed algorithm to control the BG concentration within the range of normoglycaemia in terms of accuracy, stability, quick damping and robustness.

Automated Tripod Leveling and Parameter Estimation for a Granular-fill Insulation Distributing Robot

In this paper, an approach to modeling, controller design, and parameter estimation for a self-leveling tripod base is presented. The foldable tripod requires manual set-up and its exact leg positions are therefore unknown, although knowledge of the leg configuration is needed for motion planning and also allows better control of the leveling task. An analysis of the geometry is presented, enabling parameter estimation based on finding the tilt axes of the tripod by moving both actuators separately in sequence. This is then generalized for the estimation from an arbitrary movement of the actuators. Further, a linear state-space model is derived with the estimated parameters, which serves as the basis for LQG controller design. The tripod configuration estimation is experimentally verified and provides sufficient accuracy for both the motion planning and leveling tasks. By using a linear Kalman filter, the leveling control achieves good results despite significant measurement errors caused by structural vibrations.

Reduced order LQG control design for port Hamiltonian systems

The aim of this paper is to propose a reduced order control design method for large scale port Hamiltonian systems. To this end, a structure preserving reduction method and a modified LQG control design are combined to derive a reduced order model suitable for control purposes. We first recall the structure preserving reduction method for port Hamiltonian systems called effort constraint method and characterize the error bound associated to this reduction method. We then give sufficient conditions for non-standard LQG design which allow to design a passive controller equivalent to the control by interconnection of port Hamiltonian systems. This LQG method allows to define an LQG balanced realization by computing the LQG Gramians, the effort-constraint method is then used to derive a reduced order port Hamiltonian system and to design a reduced order passive LQG controller. Finally, the method is illustrated in simulation on a mass–spring–damper system. The performances of the reduced order controller are compared to the results obtained with a full order passive LQG controller.

Reduced order controller design for Timoshenko beam: A port Hamiltonian approach

This paper deals with the structure and passivity preserving model reduction and the reduced order controller design for a class of distributed controlled port Hamiltonian systems - Timoshenko beam. The boundary conditions of the beam lead to physical constraints which are hardly considered in the reduction procedure. In this work we propose to use the descriptor system realization of port Hamiltonian system to conserve the physical constraints. A passive LQG control design method is proposed for this type of system. This LQG method defines a balanced coordinate which allows us to reduce the system. Using the obtained reduced model, a reduced order passive controller which stabilizes the full order system is designed using the LQG method. At last we give the numerical simulations to show the effectiveness of the proposed reduced passive controller.

Design of LQG Controller for Active Suspension without Considering Road Input Signals

As the road conditions are completely unknown in the design of a suspension controller, an improved linear quadratic and Gaussian distributed (LQG) controller is proposed for active suspension system without considering road input signals. The main purpose is to optimize the vehicle body acceleration, pitching angular acceleration, displacement of suspension system, and tire dynamic deflection comprehensively. Meanwhile, it will extend the applicability of the LQG controller. Firstly, the half-vehicle and road input mathematical models of an active suspension system are established, with the weight coefficients of each evaluating indicator optimized by using genetic algorithm (GA). Then, a simulation model is built in Matlab/Simulink environment. Finally, a comparison of simulation is conducted to illustrate that the proposed LQG controller can obtain the better comprehensive performance of vehicle suspension system and improve riding comfort and handling safety compared to the conventional one.

Decentralized Networked Control of Building Structures

AbstractThe article presents a new method for the design of decentralized networked switched controllers to mitigate the response of building structures under earthquakes. It consists of two phases. The first phase generates low‐order gain matrices based on the linear quadratic Gaussian (LQG) control design. It includes a substructural approach when the equations of motion of substructures are extracted from the equation of motion of the overall structures described by a finite element in‐plane (2‐D) model. Appropriate model reduction procedures, determination of damping as well as the selection of sensor and actuators locations and models are applied to each substructure. The sensors and actuators are implemented into the design model. Both resulting reduced‐order state space models of substructures are used for the proper LQG control design. The obtained local controllers are implemented into the overall structure to evaluate the performance of the closed‐loop system. Displacement, drift, acceleration, maximal actuator forces as well as dynamic responses on selected locations are checked. The computational originality is the method derived under the subsequent second phase, where the gain matrices computed in the first phase serve as a tool for the design of decentralized networked switched controller. The switching is realized periodically between two switched modes. Each mode corresponds with only one active local feedback loop for a certain period of time. The network parameter is the time interval of activity of each mode determined by a given protocol. Robustness of performance against packet dropouts and sensor faults is tested. A numerical example of the decentralized networked switched controller design applied on the 20‐story high‐fidelity building benchmark model is supplied. The simulation tests show the proposed method exhibits acceptable performance.

Event triggered of microgrid control with communication and control optimization

In this paper, an optimized event triggering scheme and an LQG controller design is proposed for a linearized wind turbine model in a microgrid system. The system is modeled as an event triggered system based on asynchronous-sampled data system (ASDS) approach. Networked induced delays are considered in the communication channel. Particle Swarm Optimization (PSO) is employed to optimize control and communication cost, while reducing the event rate. Simulation results are included to elucidate the proposed controller design. A comparative analysis is also presented to demonstrate the significance of optimization in event sampling.

Regeneratively-constrained LQG control of passive networks

In this paper we consider the synthesis of optimal feedback controllers for a stochastically-excited passive electromechanical network, subject to the constraint that in stationarity, the feedback law must be realizable with a regenerative actuation system. Regenerative systems are similar to passive systems but their dynamic constraints are more relaxed, in the sense that they only need to conserve energy in the stationary average sense, rather than at every time instant. In this paper, we examine the design of optimal LQG controllers for passive networks controlled with regenerative actuation. We show that this problem may be posed as a multi-objective LMI problem. We also characterize how close a regeneratively-constrained optimal LQG feedback law is to a passive transfer function. The concepts are demonstrated on a simple example related to vibration suppression.

Design And Experimental Verification Of State Predictive Lqg Controllers For Networked Control Systems

Abstract This paper considers a design problem of controllers for wire-less vehicles in the developed networked control system. The developed system consists of a wire-less vehicle and some computers which configure a computer network. The vehicle is controlled over the computer network. One of the computers is a router which has an active queue management mechanism to keep the queue size constant and to control congestion in the computer network. The dynamics of the vehicle over the computer network can be described as linear systems with an input time-delay. State predictive LQG controllers are designed to achieve the stability and better performances of the vehicle over the computer network. The efficacy of the designed controller is demonstrated in a numerical example and an experiment using the developed networked control system.

LQG control design over lossy communication links

This paper investigates the problem of linear-quadratic Gaussian feedback control over unreliable communication channels with observation noise. We consider the problems of estimation and control under two different protocols. In one protocol, the transfer control protocol-like protocol, the estimation/control unit provides acknowledgments to successfully deliver the packets. In the other protocol, the user datagram protocol (UDP)-like protocol, no acknowledgments are permitted. We address the nonlinearity for the UDP protocol using the suboptimal approach. It is shown that there is a critical value at which the system becomes unstable. These unstable properties do not only depend on the rates of the control packets but also on the eigenvalues for the considered system. Simulation of two numerical examples is carried out in detail to illustrate the theoretical analysis.

Limits of Performance in Systems with Periodic Irregular Sampling and Actuation Rates

This paper examines the limits of performance in systems with periodic irregular sampling rate when the actuation is not necessarily synchronized with the sampling. For such a system, three sampling and actuation schemes are considered: when the sampling and control rate are both regular, when they are both irregular, and when the sampling rate is irregular while the control rate is regular. To ascertain the limits of performance of this type of systems under each sampling and actuation scheme, the system is modeled as a linear periodically time-varying (LPTV) system; optimal LQG control design with a variance constraint is applied to find the smallest achievable mean variance of the performance signal subject to a constraint on the mean control effort variance. In addition, to deal with the computational delay of the controller, an innovative discretization method is proposed which does not introduce any extra states into the state space model. The proposed method is exploited to determine the performance of a hard disk drive (HDD) in track-following mode. A simulation study demonstrates that in the presence of 30% irregularity in sampling time, using the irregular sampling and regular control action scheme for the HDD achieves an RMS 3s value of the position error signal (PES) that is 40% smaller than the corresponding value achieved by using a controller provided by our industry partner, in which both the sampling and control rates are irregular.

LQG control of horizontal wind turbines for blades and tower loads alleviation

AbstractThe paper deals with the LQG controller design optimizing the amount of power produced by two bladed horizontal variable speed wind turbines. The proposed controller ensures not only an optimal operation of turbines but also enables a compromise with the minimization of the blade oscillations and with the tower bending tendency.

Integrated Optimization for Vehicle Active Suspension System Based on Vibration Control

Since the traditional design method, which is always to decide control parameters following structure parameters, cannot obtain the global optimum performances for active suspension system, this paper presents an approach to integrate structural and control parameters optimization for active suspension system based on improved genetic algorithm. The core of the approach is combining mechanical structure design and LQG controller design, regarding the structural parameters and control parameters as the optimization variables simultaneously, and using improved genetic algorithm to optimize the variables. The research results indicates that this integrated optimization method can effectively reduce the vibration of suspension and greatly improve the ride performance, handling stability of automobile and ride comfort. And the improved genetic algorithm has a better effect for solving complex engineering optimization problem.

Performance assessment of advanced supervisory–regulatory control systems with subspace LQG benchmark

In this paper, a subspace method for LQG design and performance assessment is proposed for control systems in which supervisory and regulatory controllers are employed in a cascade structure. Usually, this is the case when an advanced controller is used in a supervisory control layer on the top of the regulatory control system, resulting in a cascade control structure. The objective of this study is to provide the optimal LQG control design in the cascade control structure and also to propose a method for calculation of the LQG ‘trade-off’ curves for performance assessment. The trade-off curve provides the optimal performance limit in terms of the best achievable input and output variances. Three possible scenarios for LQG control design in this supervisory–regulatory structure are discussed in the paper. The problem formulations are presented in the subspace framework to directly derive the control law and LQG trade-off curve without need of the conventional parametric models. A simulation example is provided to demonstrate the proposed method.

Guaranteed cost LQG control for uncertain systems with a normalized coprime factor uncertainty structure

The paper extends the robust minimax LQG control design methodology to stochastic uncertain systems with a general uncertainty structure, which includes normalized coprime factor uncertainty, passive uncertainty and sector norm-bounded uncertainty as special cases. The derivation of the result uses a special parameter-dependent Girsanov measure transformation. The efficacy of the proposed methodology is illustrated using the problem of frequency locking of an optical cavity which occurs in the area of experimental quantum optics.

Iterative design of state predictive LQG controller for inertia rotor with time delay

Abstract It is known that LQG controller can be composed by using the state predictor for the input time delay system. Iterative design that improves the control performance of the closed-loop system has been proposed by repeating LQG control and closed-loop identification. In this research, the theory is verified by experiment of the swing control for the inertia rotor with an artificial time delay.